Pediatrics, Neonatology & Child Health

Abstract:Pathways to improved food and nutrition security: The role of farm production diversity in household dietary outcomes in rural areas of Pakistan

Malnutrition remains a significant global challenge, particularly in developing countries. Policymakers have increasingly focused on improving household food security and nutrition through farm production diversity (FPD). While research indicates that FPD correlates positively with reduced malnutrition, other studies emphasize the importance of market access for improved nutritional outcomes. However, this evidence varies by region and remains inconsistent. To address this knowledge gap, this study analyzed survey data from 450 smallholder farmers in Punjab, Pakistan, using regression models to examine the relationship between FPD and dietary diversity, as well as the underlying impact pathways. The findings demonstrate that FPD significantly correlates with increased household dietary diversity score (HDDS). FPD influences dietary diversification through both own-farm production and market food consumption pathways, with the own farm production pathway showing greater impact. The increase in food expenditure through own-farm production yielded a marginal return of 8% in household dietary diversity compared to 5.3% through marketing. Gender differences emerged as significant, with male-headed households showing relatively lower dietary diversity. These findings have substantial implications for countries with smallholder farming systems, providing valuable insights for the formation of agricultural policies, resource optimization, and rural development initiatives.

Abstract:Coping strategies of caregivers of children with chronic renal failure on automated peritoneal dialysis at a hospital in Gauteng, South Africa

Background: Renal failure is a common chronic condition which affect both adults and children worldwide and is characterized by a gradual loss of kidney function over time. There are several ways to treat kidney failure and one of the methods is automated peritoneal dialysis (APD), which is mainly used in children. Children with chronic renal failure are cared for by caregivers at home. When a child is diagnosed with chronic renal failure, the caregivers’ lives change drastically, and they must adapt to this change of caring for the child undergoing APD.

Abstract:Artificial Intelligence–Assisted Prediction of Neonatal Sepsis:
Advancing Infection Control Strategies in the NICU

Background:
Neonatal sepsis continues to be a major cause of neonatal morbidity and mortality worldwide,
particularly in resource-limited healthcare settings. Early diagnosis remains challenging due to
non-specific clinical presentations and the limited sensitivity of conventional diagnostic markers
during the initial stages of infection. Delays in recognition can contribute to increased mortality
and facilitate transmission of pathogens within neonatal intensive care units (NICUs). Recent
advances in Artificial Intelligence and Machine Learning have enabled the development of
predictive models capable of analyzing complex clinical datasets and identifying early risk
patterns associated with neonatal infections.

Methods:
This study performed a predictive framework using machine learning algorithms to identify
early indicators of neonatal sepsis and support infection control measures in NICUs.
Retrospective clinical and laboratory datasets were integrated, including maternal risk factors,
neonatal physiological parameters, hematological indices, inflammatory biomarkers,
antimicrobial exposure, and environmental infection control variables. Supervised learning
techniques such as random forest, gradient boosting, and neural network models will be
applied to detect patterns associated with early-onset and late-onset neonatal sepsis. The
developed predictive model will generate risk scores that can be incorporated into clinical
decision-support systems to alert clinicians and infection control teams in real time.

Results:
AI-assisted predictive modeling is expected to enhance early risk stratification for neonatal
sepsis compared with conventional clinical assessment methods. The system may enable earlier
identification of high-risk neonates, facilitate timely diagnostic testing, and support targeted
infection control interventions within NICUs. Integration of predictive analytics with hospital
surveillance systems could improve outbreak detection, optimize antibiotic stewardship, and
reduce nosocomial transmission.

Conclusion:
Artificial intelligence–driven prediction models offer significant potential to strengthen
infection surveillance and prevention strategies in neonatal care. By integrating clinical data
with infection control monitoring, these systems can enable earlier intervention, reduce
diagnostic delays, and improve neonatal outcomes. Future prospective multicenter validation
studies will be essential to establish the clinical utility and scalability of AI-assisted neonatal
sepsis prediction models.